Lip Biometric System - CSU1530 - Shoolini U

Lip Biometric System

1. Introduction to Lip Biometric Systems

Lip Biometric Systems identify or verify individuals by analyzing the unique features of their lips. The shape, texture, and movement patterns of lips provide distinctive characteristics useful for security and authentication applications.

Applications include:

2. Anatomy and Features of the Lips

The human lips possess features that are unique to each individual, making them suitable for biometric recognition.

2.1 Unique Characteristics

Distinctive features of the lips include:

2.2 Dynamics of Lip Movement

The way a person moves their lips during speech or expression adds another layer of uniqueness.

Aspects to consider:

3. Image Acquisition in Lip Biometrics

Capturing high-quality lip images or videos is crucial for accurate recognition.

3.1 Acquisition Methods

Techniques for capturing lip data include:

3.2 Challenges in Acquisition

Potential issues during data capture:

Mitigation strategies involve controlled environments and consistent capture protocols.

4. Preprocessing of Lip Images

Preprocessing enhances lip images and prepares them for feature extraction.

4.1 Image Enhancement

Improving image quality through:

4.2 Lip Detection and Segmentation

Isolating the lip region from the rest of the image.

Methods include:

import cv2
import numpy as np

# Read the image
image = cv2.imread('face_image.jpg')
# Convert to HSV color space
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Define color range for lips
lower_red = np.array([0, 50, 50])
upper_red = np.array([10, 255, 255])
# Create a mask
mask = cv2.inRange(hsv, lower_red, upper_red)
# Apply the mask
lip_region = cv2.bitwise_and(image, image, mask=mask)

5. Feature Extraction in Lip Biometrics

Extracting features from the lips to create a representative feature vector.

5.1 Geometric Features

Analyzing the shape and structure of the lips.

Features include:

5.2 Appearance-Based Features

Using pixel intensity values and texture patterns.

Methods:

DCT of a 2D image \( f(x,y) \):

$$ F(u,v) = \alpha(u) \alpha(v) \sum_{x=0}^{N-1} \sum_{y=0}^{N-1} f(x,y) \cos\left[\frac{\pi (2x + 1)u}{2N}\right] \cos\left[\frac{\pi (2y + 1)v}{2N}\right] $$

5.3 Dynamic Features

Capturing movement patterns during speech or expressions.

Techniques:

Optical flow equation:

$$ I_x u + I_y v + I_t = 0 $$

6. Matching and Classification

Comparing lip features to recognize or verify individuals.

6.1 Distance Metrics

Calculating similarity between feature vectors using:

DTW distance between sequences \( Q \) and \( C \):

$$ DTW(Q, C) = \min_{\pi} \sum_{k=1}^{K} d(q_{\pi_k}, c_{\pi_k}) $$

6.2 Classification Algorithms

Methods for assigning lip data to identities:

7. Evaluation Metrics

Assessing system performance using statistical measures.

7.1 Accuracy

The proportion of correct predictions made by the system.

Formula:

$$ \text{Accuracy} = \frac{\text{Number of Correct Predictions}}{\text{Total Number of Predictions}} $$

7.2 Precision and Recall

Measures for evaluating classification results.

Formulas:

$$ \text{Precision} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Positives}} $$

$$ \text{Recall} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}} $$

7.3 Receiver Operating Characteristic (ROC) Curve

Plotting true positive rate against false positive rate at various thresholds.

Definitions:

8. Challenges in Lip Biometrics

Factors affecting the accuracy and reliability of lip biometric systems.

8.1 Variability in Expressions

Changes in facial expressions can alter lip appearance.

Mitigation strategies:

8.2 Occlusions

Obstructions such as facial hair or masks can cover the lips.

Approaches:

8.3 Lighting Conditions

Variations in illumination can affect image quality.

Solutions:

8.4 Speech Variability

Differences in speech content and speed can impact dynamic features.

Strategies:

9. Implementation Example

An example of building a lip biometric system using DCT for feature extraction and HMM for classification.

9.1 Data Preparation

Steps involved:

  1. Collect Lip Videos: Gather a dataset of lip movement videos with labels.
  2. Preprocess Videos:
    • Extract frames and convert to grayscale.
    • Detect and segment the lip region in each frame.
  3. Normalize Frames: Resize and align lip images to a standard size.

9.2 Feature Extraction with DCT

Applying DCT to each lip frame to obtain feature vectors.

import numpy as np
import cv2
from scipy.fftpack import dct

def extract_dct_features(image, num_coefficients):
    # Apply 2D DCT
    dct_transformed = dct(dct(image.T, norm='ortho').T, norm='ortho')
    # Flatten and select top coefficients
    dct_flat = dct_transformed.flatten()
    return dct_flat[:num_coefficients]

# Example usage
num_coefficients = 50
features = []
for frame in lip_frames:
    feature_vector = extract_dct_features(frame, num_coefficients)
    features.append(feature_vector)

The feature vectors from all frames form a sequence for each video.

9.3 Classification with Hidden Markov Models (HMM)

Training HMMs for each individual to model their lip movement patterns.

from hmmlearn import hmm
import numpy as np

# Assuming features_list is a list of feature sequences for each individual
models = {}
for person_id, sequences in features_list.items():
    # Concatenate sequences
    X = np.concatenate(sequences)
    lengths = [len(seq) for seq in sequences]
    # Train HMM
    model = hmm.GaussianHMM(n_components=5, covariance_type='diag', n_iter=100)
    model.fit(X, lengths)
    models[person_id] = model

# Recognizing a new sequence
def recognize(sequence):
    scores = {}
    for person_id, model in models.items():
        score = model.score(sequence)
        scores[person_id] = score
    # Identify the person with the highest score
    identified_person = max(scores, key=scores.get)
    return identified_person

HMMs capture temporal dynamics in lip movements.

9.4 Evaluating the System

Using test sequences to assess performance.

# Test the recognition function
correct = 0
total = len(test_sequences)
for true_id, sequence in test_sequences.items():
    predicted_id = recognize(sequence)
    if predicted_id == true_id:
        correct += 1

accuracy = correct / total * 100
print(f'Accuracy: {accuracy:.2f}%')

10. Summary

Lip Biometric Systems utilize the unique features of the human lips, including shape, texture, and movement patterns, for personal identification. By understanding the processes of image acquisition, preprocessing, feature extraction, and classification, effective lip recognition applications can be developed. Challenges such as variability in expressions and occlusions can be addressed through appropriate techniques, enhancing the system's accuracy and reliability.